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Data X:
13 11 11 25 24 26 20 21 14 23 25 26 20 10 11 22 23 28 22 18 19 23 19 20 20 18 16 22 24 23 24 21 21 27 14 24 23 16 16 24 20 23 19 15 11 24 23 26 22 17 12 20 12 20 24 15 8 19 22 20 21 12 9 23 18 12 19 20 14 21 21 21 20 20 13 27 23 28 16 18 7 19 22 24 17 21 17 25 28 24 25 22 8 25 20 24 16 21 9 24 22 22 23 25 15 26 24 26 20 12 11 21 19 23 23 22 20 22 9 10 22 24 13 26 23 27 15 17 7 20 23 24 16 20 8 21 22 26 20 20 20 21 20 18 23 19 15 28 23 22 24 24 19 24 24 24 17 18 17 19 17 16 19 15 18 23 19 23 25 25 19 25 17 21 14 27 5 27 23 28 18 17 11 18 19 19 22 18 13 26 24 18 15 17 8 14 15 27 27 24 19 25 22 17 22 18 14 23 25 20 26 27 24 24 27 24 16 18 11 20 23 24 25 23 12 19 16 20 20 18 9 25 24 24 19 21 10 19 22 25 19 25 22 23 27 25 24 18 18 23 28 26 14 19 8 17 23 25 18 20 15 24 25 26 13 11 10 22 21 14 19 22 10 20 22 19 25 24 20 23 25 23 20 23 17 22 25 25 17 16 12 20 19 23 17 24 17 23 24 19 13 16 10 22 21 23 20 16 15 21 18 24 20 18 14 22 17 21 24 17 8 26 17 21 25 21 17 24 26 24 19 15 10 28 8 23 20 15 16 24 25 22 20 19 13 26 22 21 22 21 17 20 20 23 18 19 16 26 22 25 21 19 13 21 17 17 20 18 14 26 24 27 11 14 6 22 20 28 18 17 16 21 19 24 22 25 18 25 26 27 21 14 16 25 13 22 15 19 15 23 20 23 23 20 18 27 26 24 18 20 20 23 21 24 23 20 19 28 24 26 19 19 16 24 23 21 23 18 11 21 16 23 26 22 24 23 24 18 19 18 13 21 18 25 26 22 17 24 21 24 20 19 14 28 17 27 20 20 16 11 19 20 23 22 18 25 22 25 24 22 16 25 23 23 26 24 16 28 27 25 23 18 9 28 22 24 19 21 5 19 22 23 25 22 11 25 25 19 23 19 10 25 26 25 19 18 16 25 22 28 27 24 17 28 25 26 23 21 15 26 23 24 24 21 13 27 8 25 20 20 12 24 24 28 16 17 12 18 14 23 22 20 16 21 17 15 26 22 22 23 21 18 26 24 19 24 21 24 24 24 23 26 25 27 20 20 6 25 18 25 20 19 19 23 20 24 12 20 7 24 25 26 21 16 9 20 20 19 27 21 16 26 24 23 26 22 19 27 22 21 17 19 8 21 16 22 20 19 15 21 20 23 18 13 10 19 21 23 28 22 18 25 22 20 24 20 19 23 15 20 24 21 12 25 21 25 24 21 16 26 25 28 12 15 12 18 16 19 26 23 20 27 28 21 23 22 19 23 22 21 13 15 10 20 19 25 23 20 16 22 17 18 16 23 12 22 23 22 23 21 15 23 28 21 18 18 15 18 19 21 25 23 17 25 24 25 18 16 13 21 16 20 18 18 14 21 19 22 21 18 18 28 19 27 7 10 4 19 12 23 19 17 11 21 16 25 21 20 10 23 15 28 17 13 7 22 17 25 22 25 20 27 23 24 15 18 10 23 21 27 20 20 18 27 20 19 19 18 14 23 19 24 10 19 11 21 20 22 18 11 12 22 20 23 25 17 16 26 23 21 23 22 19 23 22 21 25 21 18 26 20 20 23 19 16 28 24 26 21 20 9 28 21 28 23 21 15 26 23 23 19 22 14 24 22 19 22 20 17 23 21 23 23 21 14 28 26 18 15 15 11 21 16 21 23 22 11 28 28 28 23 21 19 21 24 22 24 28 25 28 28 28 20 20 20 24 14 20 23 20 15 24 16 23 24 23 17 28 22 25 17 18 12 21 18 16 21 15 10 26 23 23 19 19 24 22 18 18 23 21 16 25 22 22 22 19 9 20 13 21 14 16 16 19 20 19 19 17 8 23 24 20 21 26 11 26 24 27 23 20 13 28 25 27 16 13 14 24 23 20 23 19 12 25 24 26 19 21 14 24 22 25 19 21 16 25 24 23 22 24 19 27 24 24 26 23 17 28 24 27 22 20 20 23 25 28 24 23 11 19 27 26 24 24 19 27 27 27 11 8 6 15 14 23 21 19 16 27 21 28 21 18 14 21 17 22 22 20 14 26 23 23 22 21 16 25 25 27 19 16 11 26 20 18 18 17 14 24 21 22 19 21 16 25 24 23 27 27 22 27 27 25 14 12 7 14 12 14 15 17 17 24 26 21 20 17 16 25 22 26 22 18 18 23 24 28 26 24 22 24 24 22 20 18 13 22 20 24 13 18 11 16 22 28 26 24 19 26 23 24 19 18 14 26 22 26 20 19 15 19 21 18 18 19 15 19 13 19 20 24 15 28 21 26 21 15 15 24 20 26 26 22 19 20 18 12 25 17 22 21 19 24 20 20 18 26 25 26 21 22 10 24 24 23
Names of X columns:
I1 I2 I3 E1 E2 E3
Type of Correlation
kendall
pearson
spearman
kendall
Chart options
Title:
R Code
par1 <- 'pearson' panel.tau <- function(x, y, digits=2, prefix='', cex.cor) { usr <- par('usr'); on.exit(par(usr)) par(usr = c(0, 1, 0, 1)) rr <- cor.test(x, y, method=par1) r <- round(rr$p.value,2) txt <- format(c(r, 0.123456789), digits=digits)[1] txt <- paste(prefix, txt, sep='') if(missing(cex.cor)) cex <- 0.5/strwidth(txt) text(0.5, 0.5, txt, cex = cex) } panel.hist <- function(x, ...) { usr <- par('usr'); on.exit(par(usr)) par(usr = c(usr[1:2], 0, 1.5) ) h <- hist(x, plot = FALSE) breaks <- h$breaks; nB <- length(breaks) y <- h$counts; y <- y/max(y) rect(breaks[-nB], 0, breaks[-1], y, col='grey', ...) } bitmap(file='test1.png') pairs(t(y),diag.panel=panel.hist, upper.panel=panel.smooth, lower.panel=panel.tau, main=main) dev.off() load(file='createtable') n <- length(y[,1]) n a<-table.start() a<-table.row.start(a) a<-table.element(a,paste('Correlations for all pairs of data series (method=',par1,')',sep=''),n+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,' ',header=TRUE) for (i in 1:n) { a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE) } a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE) for (j in 1:n) { r <- cor.test(y[i,],y[j,],method=par1) a<-table.element(a,round(r$estimate,3)) } a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable.tab') ncorrs <- (n*n -n)/2 mycorrs <- array(0, dim=c(10,3)) a<-table.start() a<-table.row.start(a) a<-table.element(a,'Correlations for all pairs of data series with p-values',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'pair',1,TRUE) a<-table.element(a,'Pearson r',1,TRUE) a<-table.element(a,'Spearman rho',1,TRUE) a<-table.element(a,'Kendall tau',1,TRUE) a<-table.row.end(a) cor.test(y[1,],y[2,],method=par1) for (i in 1:(n-1)) { for (j in (i+1):n) { a<-table.row.start(a) dum <- paste(dimnames(t(x))[[2]][i],';',dimnames(t(x))[[2]][j],sep='') a<-table.element(a,dum,header=TRUE) rp <- cor.test(y[i,],y[j,],method='pearson') a<-table.element(a,round(rp$estimate,4)) rs <- cor.test(y[i,],y[j,],method='spearman') a<-table.element(a,round(rs$estimate,4)) rk <- cor.test(y[i,],y[j,],method='kendall') a<-table.element(a,round(rk$estimate,4)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-value',header=T) a<-table.element(a,paste('(',round(rp$p.value,4),')',sep='')) a<-table.element(a,paste('(',round(rs$p.value,4),')',sep='')) a<-table.element(a,paste('(',round(rk$p.value,4),')',sep='')) a<-table.row.end(a) for (iii in 1:10) { iiid100 <- iii / 100 if (rp$p.value < iiid100) mycorrs[iii, 1] = mycorrs[iii, 1] + 1 if (rs$p.value < iiid100) mycorrs[iii, 2] = mycorrs[iii, 2] + 1 if (rk$p.value < iiid100) mycorrs[iii, 3] = mycorrs[iii, 3] + 1 } } } a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Meta Analysis of Correlation Tests',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Number of significant by total number of Correlations',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Type I error',1,TRUE) a<-table.element(a,'Pearson r',1,TRUE) a<-table.element(a,'Spearman rho',1,TRUE) a<-table.element(a,'Kendall tau',1,TRUE) a<-table.row.end(a) for (iii in 1:10) { iiid100 <- iii / 100 a<-table.row.start(a) a<-table.element(a,round(iiid100,2),header=T) a<-table.element(a,round(mycorrs[iii,1]/ncorrs,2)) a<-table.element(a,round(mycorrs[iii,2]/ncorrs,2)) a<-table.element(a,round(mycorrs[iii,3]/ncorrs,2)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab')
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